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1
Automatic Error Type Annotation for Arabic ...
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Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training ...
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3
HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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4
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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5
Corpus-based Open-Domain Event Type Induction ...
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6
Extracting Event Temporal Relations via Hyperbolic Geometry ...
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7
Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss ...
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8
Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention ...
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9
Corrected CBOW Performs as well as Skip-gram ...
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10
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing ...
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11
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification ...
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12
ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision ...
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13
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.14/ Abstract: Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for a more effective UKE model. In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled. From a global view, we calculate the similarity between a certain phrase and the whole document in the vector space as transitional embedding based models do. In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices. Then, we proposed a new centrality computation method to capture local salient information based on the graph structure. Finally, we further combine the modeling of global and local context for ranking. We evaluate our models on three ...
Keyword: Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://dx.doi.org/10.48448/pw2j-q918
https://underline.io/lecture/37271-unsupervised-keyphrase-extraction-by-jointly-modeling-local-and-global-context
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14
TEET! Tunisian Dataset for Toxic Speech Detection ...
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15
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents ...
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16
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations ...
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17
Lifelong Event Detection with Knowledge Transfer ...
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18
SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents ...
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19
Few-Shot Named Entity Recognition: An Empirical Baseline Study ...
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20
Low-resource Taxonomy Enrichment with Pretrained Language Models ...
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